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Energies 2017, 10(10), 1577; doi:10.3390/en10101577

Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China
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Received: 14 September 2017 / Revised: 1 October 2017 / Accepted: 3 October 2017 / Published: 12 October 2017
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Abstract

State-of-charge (SOC) estimation is essential for the safe and effective utilization of lithium-ion batteries. As the SOC cannot be directly measured by sensors, an accurate battery model and a corresponding estimation method is needed. Compared with electrochemical models, the equivalent circuit models are widely used due to their simplicity and feasibility. However, such integer order-based models are not sufficient to simulate the key behavior of the battery, and therefore, their accuracy is limited. In this paper, a new model with fractional order elements is presented. The fractional order values are adaptively updated over time. For battery SOC estimation, an unscented fractional Kalman filter (UFKF) is employed based on the proposed model. Furthermore, a dual estimation scheme is designed to estimate the variable orders simultaneously. The accuracy of the proposed model is verified under different dynamic profiles, and the experimental results indicate the stability and accuracy of the estimation method. View Full-Text
Keywords: state-of-charge; unscented Kalman filter; fractional order modeling; online estimation; lithium-ion battery state-of-charge; unscented Kalman filter; fractional order modeling; online estimation; lithium-ion battery
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Cai, M.; Chen, W.; Tan, X. Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model. Energies 2017, 10, 1577.

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